Integrative multi-layer bioinformatics of transcriptomic data for elucidating molecular mechanisms of action of polyphenol-rich extract on cardiometabolic health

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Polyphenols are bioactive compounds of plant origin, widely present in plants. Importantly, several hundreds of them are relevant for human nutrition. Numerous studies have shown that adequate daily polyphenol intake is associated with a beneficial effect on cardiometabolic health in humans. Despite the abundance of scientific evidence, the molecular mechanisms underlying these effects are not completely understood, which results from the diversity of polyphenols in the human diet, their complex metabolism in the human body, and finally, the complexity and interindividual variability in their cellular effects. Advancements in our understanding of polyphenols and their metabolism in the human body, should be complemented with detailed, in-depth studies aimed at investigating their cellular and molecular targets. In this context, modern omics technologies offer considerable exploratory advantages over classical targeted analytical approaches. Here, the latest advancements in global transcriptomics represent a particular challenge since they provide a wealth of data that includes not only the protein-coding transcriptome, but also non-coding RNAs such as miRNAs, lncRNAs, and circRNAs. Recent human intervention study with a polyphenol-rich extract has demonstrated modulation of more than 200 protein-coding genes, six miRNAs, and more than 200 lncRNAs. By analyzing the targets of non-coding RNAs and integrating these data with the data on the modulation of global gene expression of protein-coding RNAs, we identified the key affected cellular pathways, including the cAMP signalling pathway, MAPK signalling pathway, Ras signalling pathway, adrenergic signalling in cardiomyocytes, and retrograde endocannabinoid signalling. We further built and visualized an integrative network of differentially expressed protein-coding genes, TFs that potentially regulate their expression, miRNAs and lncRNAs whose expression was modulated by the polyphenol-rich extract, and their targets. With this analysis, we identified key genes that are modulated at all three levels: mRNA, miRNA, and lncRNA. In conclusion, integrative multi-layer bioinformatics of transcriptomic data has the capacity to provide evidence for key molecular targets underlying the beneficial health effects of polyphenols. In future studies, these data can be utilized for the identification of relevant gene polymorphisms that are potentially involved in determining interindividual variability in the effects of polyphenols, thereby paving the way towards personalized dietary recommendations.

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